An Informative Feature Selection Method Based on Sparse PCA for VHR Scene Classification

Understanding the scenes provided by very high resolution satellite (VHR) imagery has become a critical task. In this letter, we propose a new informative feature selection method for VHR scene classification. First, scale-invariant feature transform and speeded up robust feature operators are used to extract local features from the original VHR images to construct a visual dictionary. A sparse principal component analysis (sPCA) is then adopted to learn a set of informative features from the visual dictionary for each category. Finally, the scenes are represented by sparse informative low-level features. We conducted experiments on the University of California at Merced data set containing 21 different areal scene categories with submeter resolution and the Sydney data set containing seven land-use categories with 0.5-m spatial resolution. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods even without saliency detection.